OpenAI Deep Research API Service

An OpenAI Deep Research API Service is a deep research API that is an OpenAI API service that can support OpenAI deep research tasks.

AKA: OpenAI Research Agent API, OpenAI Research Synthesis API, OpenAI Multi-Step Research API.
Context:
- It can (typically) access OpenAI Deep Research Models via OpenAI responses endpoint using OpenAI deep research model strings.
- It can (typically) decompose OpenAI Deep Research Queryes into OpenAI deep research sub-questions through OpenAI deep research planning algorithms.
- It can (typically) orchestrate OpenAI Deep Research Tools including OpenAI web search preview tools and OpenAI code interpreter tools.
- It can (typically) execute OpenAI Deep Research Web Searches using OpenAI web_search_preview tool for OpenAI deep research information gathering.
- It can (typically) generate OpenAI Deep Research Reports with OpenAI deep research inline citations and OpenAI deep research annotations.
- It can (typically) run OpenAI Deep Research Code Analysis through OpenAI code_interpreter tool for OpenAI deep research data processing.
- It can (typically) execute OpenAI Deep Research Workflows through OpenAI deep research multi-step reasoning with OpenAI deep research transparency.
- It can (typically) process OpenAI Deep Research Requests via OpenAI responses endpoints using OpenAI deep research models.
- It can (typically) support OpenAI Deep Research Background Mode via OpenAI deep research asynchronous execution with OpenAI deep research webhook notifications.
- It can (typically) integrate OpenAI Deep Research MCP Tools through OpenAI Model Context Protocol for OpenAI deep research custom data sources.
- It can (typically) manage OpenAI Deep Research Context using OpenAI deep research large context windows for OpenAI deep research state maintenance.
- It can (typically) produce OpenAI Deep Research Citation with OpenAI deep research source metadata including OpenAI deep research URLs and OpenAI deep research character offsets.
- It can (typically) expose OpenAI Deep Research Reasoning Traces with OpenAI deep research intermediate events for OpenAI deep research audit trails.
- ...
- It can (often) integrate OpenAI Deep Research Custom Data Sources through OpenAI Model Context Protocol for OpenAI deep research enterprise integration.
- It can (often) handle OpenAI Deep Research Asynchronous Tasks with OpenAI deep research webhooks for OpenAI deep research background processing.
- It can (often) maintain OpenAI Deep Research Context Windows of OpenAI deep research large token capacity (up to 200k tokens).
- It can (often) produce OpenAI Deep Research Structured Outputs in OpenAI deep research markdown format with OpenAI deep research sections.
- It can (often) process OpenAI Deep Research Requests with OpenAI deep research developer prompts and OpenAI deep research user querys.
- It can (often) track OpenAI Deep Research Progress through OpenAI deep research reasoning summary with OpenAI deep research intermediate steps.
- It can (often) handle OpenAI Deep Research Long Tasks via OpenAI deep research background flag for OpenAI deep research timeout prevention.
- It can (often) format OpenAI Deep Research Outputs as OpenAI deep research structured documents in OpenAI deep research markdown format.
- It can (often) stream OpenAI Deep Research Events through OpenAI Agents SDK for OpenAI deep research live progress updates.
- It can (often) accept OpenAI Deep Research File Uploads via OpenAI Files API for OpenAI deep research multimodal analysis.
- It can (often) implement OpenAI Deep Research Cost Control through OpenAI deep research model selection between OpenAI deep research o3 and OpenAI deep research o4-mini.
- It can (often) validate OpenAI Deep Research Sources through OpenAI deep research cross-reference and OpenAI deep research fact-checking.
- ...
- It can range from being an OpenAI O4-mini Deep Research API to being an OpenAI O3 Deep Research API, depending on its OpenAI deep research model complexity.
- It can range from being a Synchronous OpenAI Deep Research API to being an Asynchronous OpenAI Deep Research API, depending on its OpenAI deep research execution mode.
- It can range from being a Basic-Cost OpenAI Deep Research API to being a Premium-Cost OpenAI Deep Research API, depending on its OpenAI deep research pricing tier.
- It can range from being a Quick OpenAI Deep Research API to being an Exhaustive OpenAI Deep Research API, depending on its OpenAI deep research depth configuration.
- It can range from being a Low-Cost OpenAI Deep Research API to being a High-Cost OpenAI Deep Research API, depending on its OpenAI deep research pricing tier.
- It can range from being a Single-Query OpenAI Deep Research API to being a Multi-Agent OpenAI Deep Research API, depending on its OpenAI deep research orchestration complexity.
- It can range from being a Text-Only OpenAI Deep Research API to being a Multimodal OpenAI Deep Research API, depending on its OpenAI deep research input capability.
- It can range from being a Public-Data OpenAI Deep Research API to being a Enterprise-Data OpenAI Deep Research API, depending on its OpenAI deep research data source integration.
- ...
- It can integrate with OpenAI Agents SDK for OpenAI deep research multi-agent orchestration.
- It can utilize OpenAI Files API for OpenAI deep research multimodal input.
- It can connect to OpenAI Platform Services for OpenAI deep research enterprise features.
- It can utilize OpenAI API Key for OpenAI deep research authentication.
- It can employ OpenAI Python SDK for OpenAI deep research client integration.
- It can leverage OpenAI API Endpoints for OpenAI deep research request handling.
- It can implement OpenAI Webhooks for OpenAI deep research event notification.
- It can interface with OpenAI Dashboard for OpenAI deep research webhook configuration.
- It can support OpenAI Zero-Retention Mode for OpenAI deep research data privacy.
- ...
Example(s):
Counter-Example(s):
- Standard Web Search APIs, which retrieve search results without OpenAI deep research synthesis or OpenAI deep research multi-step reasoning.
- Basic ChatGPT APIs, which provide single-turn responses without OpenAI deep research live web access or OpenAI deep research citation verification.
- OpenAI Chat Completions API, which lacks OpenAI deep research multi-step reasoning and OpenAI deep research web search capability.
- OpenAI Assistants API, which lacks OpenAI deep research specialized models and OpenAI deep research report generation.
- Simple RAG Systems, which perform single-round retrieval without OpenAI deep research iterative planning or OpenAI deep research multi-hop reasoning.
- Domain-Specific Research Tools, which lack OpenAI deep research web integration and OpenAI deep research autonomous synthesis capability.
- Google Deep Research API, which uses Google search integration rather than OpenAI deep research web_search_preview tool.
- Perplexity Deep Research, which provides concise summary rather than OpenAI deep research comprehensive reports.
See: Deep Research API, OpenAI API, AI Research Agent, Model Context Protocol, OpenAI Agents SDK, OpenAI o3 Model, OpenAI o4-mini Model, OpenAI Responses Endpoint, AI Research System, OpenAI Webhook System, OpenAI Files API, OpenAI Python SDK.

References

2025-01-27

Deep Research https://chatgpt.com/s/dr_6861a3da34b8819199ca05cd478e523c
- Question: What is the OpenAI Deep Research API and how does it support OpenAI deep research tasks?
- Answer: The OpenAI Deep Research API is a specialized API designed to automate complex research workflows that require multi-step reasoning, planning, tool use, and synthesis of information from the real world. It takes a high-level query and returns a structured, citation-rich research report by leveraging an agentic model that can decompose the task, perform web searches, run code for analysis, and integrate findings into a coherent answer.
  - Core Capabilities:
    - Design Philosophy: The Deep Research API is not just a single LLM prompt/response; it is an agent-based system built for depth, nuance, and trust. When a request is sent, the underlying model autonomously breaks down the query into sub-tasks, uses tools (like web browsing or code execution) as needed, and then synthesizes a final report with evidence.
    - Models and Access: Developers access Deep Research through the new responses API endpoint (rather than the standard chat/completions endpoints). Two model variants power the API at launch:
      - o3-deep-research-2025-06-26 (O3): The flagship model optimized for the highest-quality, in-depth synthesis. It has the maximum context window (≈200k tokens) and can produce very long outputs (~100k tokens).
      - o4-mini-deep-research-2025-06-26 (O4-mini): A lighter, faster model for latency-sensitive uses. It trades some depth for efficiency and cost-effectiveness.
  - Key Capabilities and Tooling:
    - Autonomous Multi-Step Reasoning: The core of Deep Research is an agent that can plan and reason in multiple steps. It will decompose the user's query into sub-questions, formulate a research plan, and iterate through different stages (searching, reading, calculating) before final synthesis.
    - Web Browsing and Information Gathering: The API has native access to the public internet via an integrated Web Search tool (OpenAI's web_search_preview tool). The research agent will automatically perform web searches to find up-to-date information, articles, or data relevant to the query.
    - Code Execution for Data Analysis (Code Interpreter): The Deep Research agent can invoke a sandboxed Python interpreter (the same Code Interpreter toolkit from ChatGPT) to perform computations, data analysis, or even generate charts based on retrieved data.
    - Structured Reports with Inline Citations: Rather than a raw text completion, the output of a deep research query is a well-structured report typically in Markdown format. All factual claims in the report are backed by inline citations that reference their source.
    - Transparency and Intermediate Steps: The Deep Research API exposes the entire reasoning trace of the agent. Along with the final report, the API response includes a sequence of intermediate events or steps the agent took.
    - Large Context Window: The deep research models support very large context lengths (on the order of 100k+ tokens). This means the agent can handle a vast amount of input information.
    - Asynchronous Execution & Webhooks: Deep Research tasks can be long-running, often taking several minutes (5–30 minutes per query is common for a thorough report). The API therefore supports asynchronous operation with webhooks.
    - Custom Data and Knowledge Integration (MCP): While web search covers public information, many use-cases require incorporating private or custom data. The Deep Research API allows this via the Model Context Protocol (MCP) integration.
  - Usage and Workflows:
    - API Endpoint and Request Format: Developers call the Deep Research API via the client.responses.create() method (in the OpenAI SDK) or the REST /v1/responses endpoint. It expects an input list of messages with roles like "developer" and "user".
    - Asynchronous Workflow: Most Deep Research calls should be run in the background to avoid timeouts. The API will immediately return a placeholder (with an ID for the response).
    - Streaming Progress (Agents SDK): If developers want a more interactive or iterative experience, they can use the OpenAI Agents SDK in Python.
    - Output Parsing: After a Deep Research job completes, the result is available in a structured format. The response object has an output list, where the last item is the final report.
  - Model Performance and Configuration:
    - O3-Deep-Research Performance: The top-tier model for deep research. It's described as OpenAI's "most powerful deep research model", designed for the highest level of reasoning and precision. Official pricing was around $10 per 1M input tokens and $40 per 1M output tokens.
    - O4-Mini Performance: A "mini" deep research model, focusing on faster completion and lower cost while still maintaining a high level of intelligence. O4-mini's pricing was roughly 1/4 of O3's.
  - Example Use Cases:
    - Healthcare & Scientific Research: A pharmaceutical company or healthcare consultancy could use Deep Research to investigate the economic impact of a new drug.
    - Legal Analysis and Due Diligence: Law firms have begun integrating Deep Research for tasks like case law review or contract analysis.
    - Market and Competitive Intelligence: Businesses can use Deep Research to automatically gather intelligence on competitors or market trends.
    - Travel Planning and Recommendations: An interesting consumer-oriented use is using Deep Research to plan travel or events in detail.
    - Financial & Data Analysis Reports: A finance team could use the API to generate an investment research report on a company or sector.
    - Education and Study Support: Students or researchers can use it to get a primer on a topic complete with references for further reading.
  - Comparison with Other Deep Research Tools:
    - Cost Comparison: OpenAI Deep Research uses usage-based API pricing with high cost for heavy use (ChatGPT "Pro" plan ~$200/mo for 120 queries). Google's Gemini likely included in Google's services (~$20/mo). Perplexity offers free tier (~5 queries/day) and Pro plan ~$20/mo.
    - Output Depth: OpenAI produces comprehensive, detailed reports with multiple sections. Google provides concise reports focusing on key findings. Perplexity delivers structured but concise summaries.
    - Speed: OpenAI is slowest (5–30 minutes per complex query). Google is moderate (often under 15 minutes). Perplexity is fastest (typically 2–4 minutes).
- Citations:

[1] Introduction to deep research in the OpenAI API - OpenAI Cookbook. https://cookbook.openai.com/examples/deep_research_api/introduction_to_deep_research_api
[2] OpenAI Releases New Model for Deep Research API: o3/o4-mini-deep research - AIBase. https://www.aibase.com/news/19300
[3] OpenAI Deep Research API - AI Engineer Guide. https://aiengineerguide.com/blog/openai-deep-research-api/
[4] A Developer's Guide to the OpenAI Deep Research API - Apidog. https://apidog.com/blog/openai-deep-research-api/
[5] OpenAI Deep Research API - GM-RKB. https://www.gabormelli.com/RKB/OpenAI_Deep_Research_API
[6] OpenAI Deep Research: How it Compares to Perplexity and Gemini - Helicone. https://www.helicone.ai/blog/openai-deep-research
[7] Deep Research (Beta) - Vectorize Docs. https://docs.vectorize.io/rag-pipelines/deep-research/
[8] Harvey Launches Deep Research For Legal - Artificial Lawyer. https://www.artificiallawyer.com/2025/06/27/harvey-launches-deep-research-for-legal/
[9] Deep Research API with the Agents SDK - OpenAI Cookbook. https://cookbook.openai.com/examples/deep_research_api/introduction_to_deep_research_api_agents
[10] Using OpenAI Webhooks to Handle Long-Running Tasks Efficiently - AI Engineer Guide. https://aiengineerguide.com/blog/openai-webhooks/

OpenAI Deep Research API Service

References

2025-01-27

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